This research introduces a novel technique for determining numerous fusion score levels that works with many datasets and purposes. Each of the four system pieces works together. These are Feature Engineering, Ensemble Learning, deep neural networks (DNNs), and Transfer Learning. In feature engineering, raw data is totally transformed. This stage stresses the importance of PCA and MI for predictive power. AdaBoost is added during ensemble learning. It repeatedly teaches weak learners and adjusts weights depending on errors to create a strong ensemble model. Weighted input processing, ReLU activation, and dropout layers smoothly integrate DNNs. These reveal minor data patterns and correlations. In transfer learning (fine-tuning), a trained model is modified for the feature-engineered dataset. In comparative testing, the recommended technique had greater accuracy, precision, recall, F1 score, AUC-ROC, and training duration. Efficiency measures reduce reasoning time, memory, parameter count, model size, and energy utilization. Visualizations demonstrate resource consumption, method scores, and reasoning time distribution in research. This mathematical framework improves multilayer fusion score level computations, performs well, and is versatile in many scenarios, making it a good choice for large and diverse datasets.

In order to minimize the power losses and improve the voltage profile of the distribution network, introduction of production decentralized, also called decentralized generators or distributed generators (DG), in distribution network plays an important role. The optimal placement and sizing of DG is necessary for profound DG potential. To solve this combinatorial problem, a radial basis function neural network based optimization technique is proposed in this paper. Training and Learning of the Neural Network (NN) has profuse importance while dealing with the real problems, however, the computation time associated with the progression put a question mark on the effectiveness of the approach. RBFNN with conventional learning algorithms does not achieve the desired speed and performance in the training process. To overcome this difficulty a heuristic technique Particle Swarm Optimization (PSO) is employed for efficient learning of the RBFNN. Network tested on IEEE-69bus system is used to evaluate the effectiveness of this method. The convergence of RBFNN-PSO is compared with Conventional RBFNN

This paper proposes an effective supervised learning approach for static security assessment of a large power system. Supervised learning approach employs least square support vector machine (LS-SVM) to rank the contingencies and predict the system severity level. The severity of the contingency is measured by two scalar performance indices (PIs): line MVA performance index (PI_MVA) and Voltage-reactive power performance index (PI_VQ). SVM works in two steps. Step I is the estimation of both standard indices (PI_MVA and PI_VQ) that is carried out under different operating scenarios and Step II contingency ranking is carried out based on the values of PIs. The effectiveness of the proposed methodology is demonstrated on IEEE 39-bus (New England system). The approach can be beneficial tool which is less time consuming and accurate security assessment and contingency analysis at energy management center.

In deregulated environment voltage stability has become very important factor for the purpose of analysis. In this paper some important features associated with voltage stability use in power system have discussed. Line Stability index is used for estimation of the maximum loadability and in other words index is used to recognise the weak bus in electrical power system. In this paper Artificial Neural Networks (ANNs) are used for assessment of voltage stability or to confirm secure and insecure mode of the power system. The input data of neural network are yield from the Newton-Raphson (NR) load flow analysis in the platform of MATLAB R2015b. The result obtained from the N-R method also validates through Feed-Forward Back Propagation (FFBP) Layer Recurrent (LR) and Radial Basis Function Network (RBFN) in terms of accuracy to foresee the status of the power system. The effectiveness of the analyzed methods is validated through IEEE 14 test system and IEEE 30 test bus system, using Fast Voltage Stability Index (FVSI).